Arc-consistency and arc-consistency again
Artificial Intelligence
Improving domain filtering using restricted path consistency
CAIA '95 Proceedings of the 11th Conference on Artificial Intelligence for Applications
A Strong Local Consistency for Constraint Satisfaction
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Efficient constraint propagation engines
ACM Transactions on Programming Languages and Systems (TOPLAS)
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Domain filtering consistencies
Journal of Artificial Intelligence Research
A study of residual supports in arc consistency
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Using inference to reduce arc consistency computation
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Optimal and suboptimal singleton arc consistency algorithms
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
A greedy approach to establish singleton arc consistency
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
An optimal coarse-grained arc consistency algorithm
Artificial Intelligence
Decompositions of all different, global cardinality and related constraints
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Improving the performance of maxRPC
CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Neighborhood inverse consistency preprocessing
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
CP'06 Proceedings of the 12th international conference on Principles and Practice of Constraint Programming
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Max Restricted Path Consistency (maxRPC) is a local consistency for binary constraints that enforces a higher order of consistency than arc consistency. Despite the strong pruning that can be achieved, maxRPC is rarely used because existing maxRPC algorithms suffer from overheads and redundancies as they can repeatedly perform many constraint checks without triggering any value deletions. In this paper we propose and evaluate techniques that can boost the performance of maxRPC algorithms by eliminating many of these overheads and redundancies. These include the combined use of two data structures to avoid many redundant constraint checks, and the exploitation of residues to quickly verify the existence of supports. Based on these, we propose a number of closely related maxRPC algorithms. The first one, maxRPC3, has optimal O(end 3) time complexity, displays good performance when used stand-alone, but is expensive to apply during search. The second one, maxRPC3 rm , has O(en 2 d 4) time complexity, but a restricted version with O(end 4) complexity can be very efficient when used during search. The other algorithms are simple modifications of maxRPC3 rm . All algorithms have O(ed) space complexity when used stand-alone. However, maxRPC3 has O(end) space complexity when used during search, while the others retain the O(ed) complexity. Experimental results demonstrate that the resulting methods constantly outperform previous algorithms for maxRPC, often by large margins, and constitute a viable alternative to arc consistency on some problem classes.